Recent innovations in deep learning approaches substantially improved spoken dialog
systems in both academic research and industry applications. Speech recognition
systems now regularly leverage neural network models to achieve near human performance [1]}, [2]}, [3]}, .
Modern systems increasingly use architecture themes including attention and end-to-end recurrent neural networks to encode few assumptions and rapidly adapt to new data [4]}, [5]}, [6]}, [7]}, [8]}, [9]}. In parallel, approaches to spoken and text-based dialog
systems increasingly leverage neural networks for dialog management and
state representation [10]}, [11]}, [12]}.
